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def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
if (torch.min(y) < (- 1.0)):
print('min value is ', torch.min(y))
if (torch.max(y) > 1.0):
print('max value is ', torch.max(y))
global mel_basis, hann_window
dtype_device = ((str(y... |
def search_func(model, column, key, iter_, handledirs):
check = model.get_value(iter_, 0)
if (check is None):
return True
elif ((not handledirs) or (os.sep not in key)):
check = (os.path.basename(check) or os.sep)
return ((key not in check.lower()) and (key not in check)) |
def main():
keylist = [('system\\currentcontrolset\\control\\timezoneinformation', 'OPS_EXTRA_TIMEZONE_KEY', ONE_DAY, True), ('SYSTEM\\CurrentControlSet\\Enum\\USB', 'OPS_USB_USB_KEY', ONE_DAY, True), ('SYSTEM\\CurrentControlSet\\Enum\\USBSTOR', 'OPS_USB_USBSTOR_KEY', ONE_DAY, True), ('Software\\Policies\\Microsoft... |
class ExtensionTests(unittest.TestCase):
def test_encode(self):
with self.assertRaises(NotImplementedError):
Extension().encode(Frame(Opcode.TEXT, b''))
def test_decode(self):
with self.assertRaises(NotImplementedError):
Extension().decode(Frame(Opcode.TEXT, b'')) |
def get_crystal_class(cell, ops=None, tol=SYMPREC):
if (ops is None):
ops = search_space_group_ops(cell, tol=tol)
rotations = []
for op in ops:
rotations.append(op.rot)
rotations = np.unique(np.asarray(rotations), axis=0)
maps = {(- 6): 0, (- 4): 1, (- 3): 2, (- 2): 3, (- 1): 4, 1: 5... |
('hyperlink.contains_page_break is {value}')
def then_hyperlink_contains_page_break_is_value(context: Context, value: str):
actual_value = context.hyperlink.contains_page_break
expected_value = {'True': True, 'False': False}[value]
assert (actual_value == expected_value), f'expected: {expected_value}, got: ... |
def weights_init_normal(m):
classname = m.__class__.__name__
if (classname.find('Conv') != (- 1)):
init.normal(m.weight.data, 0.0, 0.02)
elif (classname.find('Linear') != (- 1)):
init.normal(m.weight.data, 0.0, 0.02)
elif (classname.find('BatchNorm') != (- 1)):
init.normal(m.weig... |
def _reduce_terms(terms, stabilizer_list, manual_input, fixed_positions):
if (manual_input is False):
fixed_positions = []
for (i, _) in enumerate(stabilizer_list):
selected_stab = list(stabilizer_list[0].terms)[0]
if (manual_input is False):
for qubit_pauli in selected_stab:... |
class ResNetShortCut(nn.Module):
def __init__(self, in_channels: int, out_channels: int, stride: int=2):
super().__init__()
self.convolution = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False)
self.normalization = nn.BatchNorm2d(out_channels)
def forward(self... |
.supported(only_if=(lambda backend: backend.pkcs7_supported()), skip_message='Requires OpenSSL with PKCS7 support')
class TestPKCS7Loading():
def test_load_invalid_der_pkcs7(self, backend):
with pytest.raises(ValueError):
pkcs7.load_der_pkcs7_certificates(b'nonsense')
def test_load_invalid_p... |
def apply_find_items_viewed(df, item_mappings):
import pandas as pd
import numpy as np
df = df.sort_values(by=['wcs_user_sk', 'tstamp', 'wcs_sales_sk', 'wcs_item_sk'], ascending=[False, False, False, False])
df.reset_index(drop=True, inplace=True)
df['relevant_flag'] = ((df.wcs_sales_sk != 0) & (df.... |
class CICleanSetup(object):
def setup_method(self):
self.ci_env = (os.environ.get('CI') == 'true')
reload(pysat)
self.saved_path = copy.deepcopy(pysat.params['data_dirs'])
if (not self.ci_env):
pytest.skip('Skipping local tests to avoid breaking user setup')
else:... |
class CustomDatasetDataLoader(BaseDataLoader):
def name(self):
return 'CustomDatasetDataLoader'
def initialize(self, opt):
BaseDataLoader.initialize(self, opt)
self.dataset = create_dataset(opt)
self.dataloader = torch.utils.data.DataLoader(self.dataset, batch_size=opt.batchSize,... |
def evaluate(args):
opt = vars(args)
opt['dataset_splitBy'] = ((opt['dataset'] + '_') + opt['splitBy'])
if (args.cfg_file is not None):
cfg_from_file(args.cfg_file)
if (args.set_cfgs is not None):
cfg_from_list(args.set_cfgs)
print('Using config:')
pprint.pprint(cfg)
data_jso... |
def qdot_sideinfo_simple(qp: QP, env_sys: System):
dp_pos = (qp.vel * env_sys.active_pos)
def op(qp_ang: jnp.ndarray, qp_rot: jnp.ndarray, active_rot: jnp.ndarray):
return jnp.matmul(ang_to_quat((qp_ang * active_rot)), qp_rot)
return (dp_pos, op(qp.ang, qp.rot, env_sys.active_rot)) |
def _load_or_init_impl(session, method_order, allow_drop_layers, allow_lr_init=True):
for method in method_order:
if (method == 'best'):
ckpt_path = _checkpoint_path_or_none('best_dev_checkpoint')
if ckpt_path:
log_info('Loading best validating checkpoint from {}'.for... |
def pytest_runtest_teardown(item, nextitem):
reruns = get_reruns_count(item)
if (reruns is None):
return
if (not hasattr(item, 'execution_count')):
return
_test_failed_statuses = getattr(item, '_test_failed_statuses', {})
if ((item.execution_count <= reruns) and any(_test_failed_stat... |
class RagRetriever():
def __init__(self, config, question_encoder_tokenizer, generator_tokenizer, index=None, init_retrieval=True):
self._init_retrieval = init_retrieval
requires_backends(self, ['datasets', 'faiss'])
super().__init__()
self.index = (index or self._build_index(config)... |
def generate_costing_table(pyscf_mf: scf.HF, cutoffs: np.ndarray, name: str='pbc', chi: int=10, beta: int=20, dE_for_qpe: float=0.0016, energy_method: str='MP2') -> pd.DataFrame:
kmesh = kpts_to_kmesh(pyscf_mf.cell, pyscf_mf.kpts)
cc_inst = build_cc_inst(pyscf_mf)
exact_eris = cc_inst.ao2mo()
if (energy... |
class TaskRc(dict):
UDA_TYPE_MAP = {'date': DateField, 'duration': DurationField, 'numeric': NumericField, 'string': StringField}
def __init__(self, path=None, overrides=None):
self.overrides = (overrides if overrides else {})
if path:
self.path = os.path.normpath(os.path.expanduser(... |
class VmfsFileSystem(LoopbackFileSystemMixin, MountFileSystem):
type = 'vmfs'
aliases = ['vmfs_volume_member']
guids = ['2AE031AA-0F40-DB11-9590-000C2911D1B8']
def mount(self):
self._make_mountpoint()
self._find_loopback()
try:
_util.check_call_(['vmfs-fuse', self.loo... |
.parametrize('screenshot_manager', [{}, {'type': 'box'}, {'type': 'line'}, {'type': 'line', 'line_width': 1}, {'start_pos': 'top'}], indirect=True)
def ss_memorygraph(screenshot_manager):
widget = screenshot_manager.c.widget['memorygraph']
widget.eval(f'self.values={values}')
widget.eval('self.draw()')
... |
class InteractivePolicy(Policy):
def __init__(self, env, agent_index):
super(InteractivePolicy, self).__init__()
self.env = env
self.move = [False for i in range(4)]
self.comm = [False for i in range(env.world.dim_c)]
env.viewers[agent_index].window.on_key_press = self.key_pr... |
class Projector():
def __init__(self, projector_model: Model, video_chunk_iterator_provider: VideoChunkIteratorProvider) -> None:
self._projector_model = projector_model
self._video_chunk_iterator_provider = video_chunk_iterator_provider
def project(self, video_features: np.ndarray) -> np.ndarra... |
class _VCTKBaseDataSource(FileDataSource):
def __init__(self, data_root, speakers, labelmap, max_files):
self.data_root = data_root
for idx in range(len(speakers)):
if (speakers[idx][0] == 'p'):
speakers[idx] = speakers[idx][1:]
if (speakers == 'all'):
... |
def create_model(feat_dim, num_classes=1000, scale=16, stage1_weights=False, dataset=None, shot_phase='stage1', test=False, *args):
print('Loading Dot Product Classifier.')
print(num_classes, feat_dim)
clf = CosNorm_Classifier(num_classes, feat_dim, scale)
if (not test):
if stage1_weights:
... |
_tf
class TFTransfoXLModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = ((TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ())
all_generative_model_classes = (() if is_tf_available() else ())
pipeline_model_mapp... |
class IntegratorDiag(Integrator):
integrator_options = {'eigensolver_dtype': 'dense'}
support_time_dependant = False
supports_blackbox = False
method = 'diag'
def __init__(self, system, options):
if (not system.isconstant):
raise ValueError('Hamiltonian system must be constant to... |
_fixtures(WebFixture, SessionScopeFixture)
def test_logging_in(web_fixture, session_scope_fixture):
browser = Browser(web_fixture.new_wsgi_app(site_root=SessionScopeUI))
user = session_scope_fixture.user
browser.open('/')
browser.click(XPath.link().with_text('Log in'))
browser.type(XPath.input_label... |
class DeepAdversarialMetricLearning(TrainWithClassifier):
def __init__(self, metric_alone_epochs=0, g_alone_epochs=0, g_triplets_per_anchor=100, *args, **kwargs):
super().__init__(*args, **kwargs)
self.original_loss_weights = copy.deepcopy(self.loss_weights)
self.metric_alone_epochs = metric... |
def generate_summary_html(root_dir: Path):
test_groups = get_test_groups(root_dir)
test_cases = get_test_cases(test_groups, (root_dir / 'tests'))
summary_html = ''
for type_checker in TYPE_CHECKERS:
version_file = (((root_dir / 'results') / type_checker.name) / 'version.toml')
try:
... |
def make_KeyPress_from_keydescr(keydescr):
keyinfo = KeyPress()
if ((len(keydescr) > 2) and (keydescr[:1] == u'"') and (keydescr[(- 1):] == u'"')):
keydescr = keydescr[1:(- 1)]
while 1:
lkeyname = keydescr.lower()
if lkeyname.startswith(u'control-'):
keyinfo.control = Tru... |
class TaskbarTestCases(unittest.TestCase):
def setUp(self):
Timings.defaults()
self.tm = _ready_timeout
app = Application(backend='win32')
app.start(os.path.join(mfc_samples_folder, u'TrayMenu.exe'), wait_for_idle=False)
self.app = app
self.dlg = app.top_window()
... |
def test_retry_exec_iteration_raises_on_error_not_in_retryon():
rd = RetryDecorator({'max': 3, 'retryOn': ['KeyError', 'BlahError']})
context = Context({})
mock = MagicMock()
mock.side_effect = ValueError('arb')
with patch_logger('pypyr.dsl', logging.ERROR) as mock_logger_error:
with pytest.... |
def _compute_statistics_of_path(path, model, batch_size, dims, cuda):
if path.endswith('.npz'):
f = np.load(path)
(m, s) = (f['mu'][:], f['sigma'][:])
f.close()
else:
path = pathlib.Path(path)
files = (list(path.glob('*.jpg')) + list(path.glob('*.png')))
(m, s) = ... |
def export_chunks_from_ultrastar_data(audio_filename: str, ultrastar_data: UltrastarTxtValue, folder_name: str) -> None:
print(f'{ULTRASINGER_HEAD} Export Ultrastar data as vocal chunks wav files')
create_folder(folder_name)
wave_file = wave.open(audio_filename, 'rb')
(sample_rate, n_channels) = (wave_f... |
def validate_metrics_list(metrics_list):
metric_names = [metric.get_name() for metric in metrics_list]
if (len(metric_names) != len(set(metric_names))):
raise TrackEvalException('Code being run with multiple metrics of the same name')
fields = []
for m in metrics_list:
fields += m.fields... |
def get_padding_shape(filter_shape, stride):
def _pad_top_bottom(filter_dim, stride_val):
pad_along = max((filter_dim - stride_val), 0)
pad_top = (pad_along // 2)
pad_bottom = (pad_along - pad_top)
return (pad_top, pad_bottom)
padding_shape = []
for (filter_dim, stride_val) i... |
def get_all_pods(label_selector=None):
pods = []
if label_selector:
ret = cli.list_pod_for_all_namespaces(pretty=True, label_selector=label_selector)
else:
ret = cli.list_pod_for_all_namespaces(pretty=True)
for pod in ret.items:
pods.append([pod.metadata.name, pod.metadata.namesp... |
class InlineQueryResultAudio(InlineQueryResult):
def __init__(self, audio_url: str, title: str, id: str=None, performer: str='', audio_duration: int=0, caption: str='', parse_mode: Optional['enums.ParseMode']=None, caption_entities: List['types.MessageEntity']=None, reply_markup: 'types.InlineKeyboardMarkup'=None, ... |
class TestNormalization():
def test_normalize():
with pytest.raises(TypeError):
Normalize(dict(mean=[123.675, 116.28, 103.53]), [58.395, 57.12, 57.375])
with pytest.raises(TypeError):
Normalize([123.675, 116.28, 103.53], dict(std=[58.395, 57.12, 57.375]))
target_keys ... |
class ThresholdValueTest(unittest.TestCase):
_grad()
def _test_accuracy_helper(self, labels: torch.Tensor, predictions: torch.Tensor, weights: torch.Tensor, expected_accuracy: torch.Tensor, threshold: float) -> None:
num_task = labels.shape[0]
batch_size = labels.shape[0]
task_list = []
... |
class WithDescriptors(Serialisable):
descriptor = Nested(expected_type=str)
set_tuple = NestedSet(values=('a', 1, 0.0))
set_list = NestedSet(values=['a', 1, 0.0])
set_tuple_none = NestedSet(values=('a', 1, 0.0, None))
noneset_tuple = NestedNoneSet(values=('a', 1, 0.0))
noneset_list = NestedNoneS... |
_ordering
class Condition():
def __init__(self, operator):
self.operator = operator
assert isinstance(operator, TypeConditionOperator), (('the operator ' + str(operator)) + ' should be of type TypeConditionOperator')
def _key(self):
return (str(type(self)), str(self.operator))
def __... |
class EmpiricalAPSParams(KiteParameterGroup):
def __init__(self, model, **kwargs):
scene = model.getScene()
kwargs['type'] = 'group'
kwargs['name'] = 'Scene.APS (empirical)'
KiteParameterGroup.__init__(self, model=model, model_attr='scene', **kwargs)
p = {'name': 'applied', '... |
class Lookahead(nn.Module):
def __init__(self, n_features, context):
super(Lookahead, self).__init__()
self.n_features = n_features
self.weight = Parameter(torch.Tensor(n_features, (context + 1)))
assert (context > 0)
self.context = context
self.register_parameter('bi... |
class IntelHex16bit(IntelHex):
def __init__(self, source=None):
if isinstance(source, IntelHex):
self.padding = source.padding
self.start_addr = source.start_addr
self._buf = source._buf
self._offset = source._offset
elif isinstance(source, dict):
... |
def test_get_index(textpage):
(x, y) = (60, (textpage.page.get_height() - 66))
index = textpage.get_index(x, y, 5, 5)
assert ((index < textpage.count_chars()) and (index == 0))
charbox = textpage.get_charbox(index)
char = textpage.get_text_bounded(*charbox)
assert (char == 'L') |
class OutputList(RecycleView):
def __init__(self, **kwargs):
super(OutputList, self).__init__(**kwargs)
self.app = App.get_running_app()
def update(self, outputs: Sequence['TxOutput']):
res = []
for o in outputs:
value = self.app.format_amount_and_units(o.value)
... |
class MultiplyResponse(FrequencyResponse):
responses = List.T(FrequencyResponse.T())
def __init__(self, responses=None, **kwargs):
if (responses is None):
responses = []
FrequencyResponse.__init__(self, responses=responses, **kwargs)
def get_fmax(self):
fmaxs = [resp.get_... |
def to_vertex_format(format):
if (format in wgpu.VertexFormat):
return format
elif (format in wgpu.IndexFormat):
return format
primitives = {'i1': 'sint8', 'u1': 'uint8', 'i2': 'sint16', 'u2': 'uint16', 'i4': 'sint32', 'u4': 'uint32', 'f2': 'float16', 'f4': 'float32'}
primitive = primiti... |
def rebuild_open(img: np.ndarray, kernel: np.ndarray, erode_time: int=1) -> np.ndarray:
temp_img = img.copy()
for i in range(erode_time):
temp_img = cv2.erode(temp_img, kernel)
''
dialate_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
last_img = temp_img.copy()
while True:
... |
class TestContextManagerFixtureFuncs():
def test_simple(self, pytester: Pytester) -> None:
pytester.makepyfile('\n import pytest\n \n def arg1():\n print("setup")\n yield 1\n print("teardown")\n def test_1(arg1):\n ... |
_environment_variables(model=MyHandlerEnvVars)
def my_handler(event: dict[(str, Any)], context: LambdaContext) -> dict[(str, Any)]:
env_vars: MyHandlerEnvVars = get_environment_variables(model=MyHandlerEnvVars)
return {'statusCode': HTTPStatus.OK, 'headers': {'Content-Type': 'application/json'}, 'body': json.du... |
def fast_inplace_check(fgraph, inputs):
Supervisor = pytensor.compile.function.types.Supervisor
protected_inputs = [f.protected for f in fgraph._features if isinstance(f, Supervisor)]
protected_inputs = sum(protected_inputs, [])
protected_inputs.extend(fgraph.outputs)
inputs = [i for i in inputs if ... |
.end_to_end()
.skipif((not IS_PEXPECT_INSTALLED), reason='pexpect is not installed.')
.skipif((sys.platform == 'win32'), reason='pexpect cannot spawn on Windows.')
def test_trace(tmp_path):
source = '\n def task_example():\n i = \n '
tmp_path.joinpath('task_module.py').write_text(textwrap.dedent(so... |
def data_loader(args, test_path=False, segmentation=False):
mean_vals = [0.485, 0.456, 0.406]
std_vals = [0.229, 0.224, 0.225]
input_size = (int(args.input_size), int(args.input_size))
crop_size = (int(args.crop_size), int(args.crop_size))
tsfm_train = transforms.Compose([transforms.Resize(input_siz... |
def camel_to_snake(naming, name):
if naming.prefix:
assert name.startswith(naming.prefix)
name = name[len(naming.prefix):]
if naming.suffix:
assert name.endswith(naming.suffix)
name = name[:(- len(naming.suffix))]
return re.sub('(?<!^)(?=[A-Z])', '_', name).lower() |
def glc_to_material(l, item=None):
line = l.strip().split()
name = line.pop(0)
nd = sfloat(line.pop(0))
vd = sfloat(line.pop(0))
density = sfloat(line.pop(0))
del line[:6]
del line[:2]
(a, num) = (sint(line.pop(0)), sint(line.pop(0)))
coeff = np.array([sfloat(_) for _ in line[:num]])... |
class Ametek7270(Instrument):
SENSITIVITIES = [0.0, 2e-09, 5e-09, 1e-08, 2e-08, 5e-08, 1e-07, 2e-07, 5e-07, 1e-06, 2e-06, 5e-06, 1e-05, 2e-05, 5e-05, 0.0001, 0.0002, 0.0005, 0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 1.0]
SENSITIVITIES_IMODE = {0: SENSITIVITIES, 1: [(sen * 1e-06) for sen in SENSITIVI... |
def iter_benchmark(iterator, num_iter: int, warmup: int=5, max_time_seconds: float=60) -> Tuple[(float, List[float])]:
(num_iter, warmup) = (int(num_iter), int(warmup))
iterator = iter(iterator)
for _ in range(warmup):
next(iterator)
timer = Timer()
all_times = []
for curr_iter in tqdm.t... |
def has_node_changed(task: PTask, node: (PTask | PNode)) -> bool:
node_state = node.state()
if (node_state is None):
return True
with DatabaseSession() as session:
db_state = session.get(State, (task.signature, node.signature))
if (db_state is None):
return True
return (node_... |
def main(unused_argv):
parse_cmdline_gin_configurations()
try:
checkpoint_to_reload = gin.query_parameter('LearnerConfig.checkpoint_for_eval')
except ValueError:
try:
checkpoint_to_reload = gin.query_parameter('LearnerConfig.pretrained_checkpoint')
except ValueError:
... |
class TestDetermineAttribEqOrder():
def test_default(self):
assert ((42, None, 42, None) == _determine_attrib_eq_order(None, None, None, 42))
def test_eq_callable_order_boolean(self):
assert ((True, str.lower, False, None) == _determine_attrib_eq_order(None, str.lower, False, True))
def test... |
class NELDER_MEAD(Optimizer):
_OPTIONS = ['maxiter', 'maxfev', 'disp', 'xatol', 'adaptive']
def __init__(self, maxiter: Optional[int]=None, maxfev: int=1000, disp: bool=False, xatol: float=0.0001, tol: Optional[float]=None, adaptive: bool=False) -> None:
super().__init__()
for (k, v) in list(loc... |
def start_tunnel(dsz_cmd=None):
assert verify_dsz_cmd_redirect_object(dsz_cmd), 'Given dsz_cmd must be a valid ops.cmd object for the redirect plugin.'
tunnel = verify_tunnel(id=None, dsz_cmd=dsz_cmd, return_status=False)
if (tunnel is not False):
return int(tunnel.id)
redir_obj = dsz_cmd.execut... |
class TestPairingWithSymmetries(unittest.TestCase):
def test_two_fermions(self):
bins = [[1], [2], [3], [4]]
count = 0
for pairing in pair_within_simultaneously_binned(bins):
count += 1
self.assertEqual(len(pairing), 2)
print(pairing)
self.assertEq... |
def optimize(struc, ff, optimizations=['conp', 'conp'], exe='gulp', pstress=None, path='tmp', label='_', clean=True, adjust=False):
time_total = 0
for opt in optimizations:
(struc, energy, time, error) = single_optimize(struc, ff, pstress=pstress, opt=opt, exe=exe, path=path, label=label, clean=clean)
... |
class EGICheckinOpenIdConnect(OpenIdConnectAuth):
name = 'egi-checkin'
CHECKIN_ENV = 'prod'
USERNAME_KEY = 'voperson_id'
EXTRA_DATA = [('expires_in', 'expires_in', True), ('refresh_token', 'refresh_token', True), ('id_token', 'id_token', True)]
DEFAULT_SCOPE = ['openid', 'profile', 'email', 'voperso... |
class PFS_No_Ksappend(ParserTest):
def __init__(self, *args, **kwargs):
ParserTest.__init__(self, *args, **kwargs)
self.ks = '\nlang en_US\nkeyboard us\nautopart\n'
def setUp(self):
ParserTest.setUp(self)
self._path = None
def runTest(self):
self._path = preprocessFro... |
class ScanInplaceOptimizer(GraphRewriter):
alloc_ops = (Alloc, AllocEmpty)
def add_requirements(self, fgraph):
fgraph.attach_feature(ReplaceValidate())
fgraph.attach_feature(DestroyHandler())
def attempt_scan_inplace(self, fgraph: FunctionGraph, node: Apply[Scan], output_indices: list[int]) ... |
def _iter_params_for_processing(invocation_order: Sequence[Parameter], declaration_order: Sequence[Parameter]) -> list[Parameter]:
def sort_key(item: Parameter) -> tuple[(bool, float)]:
if (item.name == 'paths'):
return (False, (- 3))
if (item.name == 'config'):
return (False... |
class BasicBlock(nn.Module):
def __init__(self, in_channels, channels, kernel_size, stride=1, padding=0, **block_kwargs):
super(BasicBlock, self).__init__()
self.conv = layers.convnxn(in_channels, channels, kernel_size, stride=stride, padding=padding)
self.relu = layers.relu()
def forwar... |
def parse_args():
parser = argparse.ArgumentParser(description='Finetune a transformers model on a text classification task')
parser.add_argument('--dataset_name', type=str, default=None, help='The name of the dataset to use (via the datasets library).')
parser.add_argument('--predict_with_generate', type=b... |
class VOTLTDataset(Dataset):
def __init__(self, name, dataset_root, load_img=False):
super(VOTLTDataset, self).__init__(name, dataset_root)
with open(os.path.join(dataset_root, (name + '.json')), 'r') as f:
meta_data = json.load(f)
pbar = tqdm(meta_data.keys(), desc=('loading ' +... |
class SubcommandsExample(cmd2.Cmd):
def __init__(self):
super().__init__()
def base_foo(self, args):
self.poutput((args.x * args.y))
def base_bar(self, args):
self.poutput(('((%s))' % args.z))
def base_sport(self, args):
self.poutput('Sport is {}'.format(args.sport))
... |
def main():
parser = argparse.ArgumentParser(description='symmetric alignment builer')
parser.add_argument('--fast_align_dir', help='path to fast_align build directory')
parser.add_argument('--mosesdecoder_dir', help='path to mosesdecoder root directory')
parser.add_argument('--sym_heuristic', help='heu... |
def main(argv=None):
op = argparse.ArgumentParser()
op.add_argument('-i', '--input', dest='input', help=_('a basis file to use for seeding the kickstart data (optional)'))
op.add_argument('-o', '--output', dest='output', help=_('the location to write the finished kickstart file, or stdout if not given'))
... |
class MobileNetV2ImageProcessor(BaseImageProcessor):
model_input_names = ['pixel_values']
def __init__(self, do_resize: bool=True, size: Optional[Dict[(str, int)]]=None, resample: PILImageResampling=PILImageResampling.BILINEAR, do_center_crop: bool=True, crop_size: Dict[(str, int)]=None, do_rescale: bool=True, ... |
def rename_key(orig_key):
if ('model' in orig_key):
orig_key = orig_key.replace('model.', '')
if ('norm1' in orig_key):
orig_key = orig_key.replace('norm1', 'attention.output.LayerNorm')
if ('norm2' in orig_key):
orig_key = orig_key.replace('norm2', 'output.LayerNorm')
if ('norm'... |
.skipif((sys.platform == 'win32'), reason='Windows only applies R/O to files')
def test_destination_not_write_able(tmp_path, capsys):
if (hasattr(os, 'geteuid') and (os.geteuid() == 0)):
pytest.skip('no way to check permission restriction when running under root')
target = tmp_path
prev_mod = target... |
class W_ComposableContinuation(W_Procedure):
errorname = 'composable-continuation'
_attrs_ = _immutable_fields_ = ['cont', 'prompt_tag']
def __init__(self, cont, prompt_tag=None):
self.cont = cont
self.prompt_tag = prompt_tag
def get_arity(self, promote=False):
from pycket.arity ... |
def optimize_module(quant_module: QcQuantizeWrapper, x: torch.Tensor, xq: torch.Tensor, params: SeqMseParams):
if quant_module.param_quantizers['weight'].use_symmetric_encodings:
per_channel_max = torch.max(quant_module.weight.abs(), dim=1)[0].detach()
per_channel_min = None
else:
per_ch... |
class LoggedInteractiveConsole(code.InteractiveConsole):
def __init__(self, _locals: Dict[(str, Any)], logpath: str) -> None:
code.InteractiveConsole.__init__(self, _locals)
self.output_file = logpath
self.pid = os.getpid()
self.pri = (syslog.LOG_USER | syslog.LOG_NOTICE)
sel... |
class PetitionCreateWizardViewTest(TestCase):
def setUpTestData(cls):
User = get_user_model()
for org in orgs:
o = Organization.objects.create(name=org)
o.save()
for user in users:
u = User.objects.create_user(user, password=user)
u.first_name ... |
class TestFileMonitor():
def test_create_delete(self, temp_dir: Path):
path = temp_dir
monitor = BasicMonitor(path)
some_file = (path / 'foo.txt')
some_file.write_text('test')
sleep(SLEEP_SECS)
run_gtk_loop()
assert monitor.changed, 'No events after creation'
... |
def _gen_efficientnet_lite(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs):
arch_def = [['ds_r1_k3_s1_e1_c16'], ['ir_r2_k3_s2_e6_c24'], ['ir_r2_k5_s2_e6_c40'], ['ir_r3_k3_s2_e6_c80'], ['ir_r3_k5_s1_e6_c112'], ['ir_r4_k5_s2_e6_c192'], ['ir_r1_k3_s1_e6_c320']]
model_kwargs = dic... |
class QuadraticProgram():
Status = QuadraticProgramStatus
def __init__(self, name: str='') -> None:
if (not name.isprintable()):
warn('Problem name is not printable')
self._name = ''
self.name = name
self._status = QuadraticProgram.Status.VALID
self._variables... |
def get_xml_type(val):
LOG.info(('Inside get_xml_type(). val = "%s", type(val) = "%s"' % (val, type(val).__name__)))
if (type(val).__name__ == 'NoneType'):
LOG.info("type(val).__name__ == 'NoneType', returning 'null'")
return 'null'
elif (type(val).__name__ == 'bool'):
LOG.info("type... |
def test_paintEvent(skip_qtbot, canvas, mocker):
event = MagicMock()
mock_painter: MagicMock = mocker.patch('PySide6.QtGui.QPainter')
canvas.paintEvent(event)
mock_painter.assert_called_once_with(canvas)
draw_ellipse: MagicMock = mock_painter.return_value.drawEllipse
draw_ellipse.assert_any_call... |
def basic_multivector_operations_2D():
Print_Function()
(ex, ey) = MV.setup('e*x|y')
print('g_{ij} =', MV.metric)
X = MV('X', 'vector')
A = MV('A', 'spinor')
X.Fmt(1, 'X')
A.Fmt(1, 'A')
(X | A).Fmt(2, 'X|A')
(X < A).Fmt(2, 'X<A')
(A > X).Fmt(2, 'A>X')
return |
class TestRandomRegionEmpirical():
def setup_method(self):
self.mexico = MEXICO.copy()
self.cards = self.mexico.groupby(by='HANSON03').count().NAME.values.tolist()
self.ids = self.mexico.index.values.tolist()
self.w = libpysal.weights.Queen.from_dataframe(self.mexico)
def test_ra... |
class LightningModel(pl.LightningModule):
def __init__(self, model, learning_rate):
super().__init__()
self.learning_rate = learning_rate
self.model = model
if hasattr(model, 'dropout_proba'):
self.dropout_proba = model.dropout_proba
self.save_hyperparameters(igno... |
class untitled(App):
def main(self):
mainContainer = Container(width=706, height=445, margin='0px auto', style='position: relative')
subContainer = HBox(width=630, height=277, style='position: absolute; left: 40px; top: 150px; background-color: #b6b6b6')
vbox = VBox(width=300, height=250)
... |
def test_lineanchors_with_startnum():
optdict = dict(lineanchors='foo', linenostart=5)
outfile = StringIO()
fmt = HtmlFormatter(**optdict)
fmt.format(tokensource, outfile)
html = outfile.getvalue()
assert re.search('<pre>\\s*<span>\\s*</span>\\s*<a id="foo-5" name="foo-5" href="#foo-5">', html) |
def get_default_image_compressor(**kwargs):
if imagecodecs.JPEGXL:
this_kwargs = {'effort': 3, 'distance': 0.3, 'decodingspeed': 1}
this_kwargs.update(kwargs)
return JpegXl(**this_kwargs)
else:
this_kwargs = {'level': 50}
this_kwargs.update(kwargs)
return Jpeg2k(*... |
class CQADupstackPhysicsRetrieval(AbsTaskRetrieval, BeIRTask):
def description(self):
return {'name': 'CQADupstackPhysicsRetrieval', 'beir_name': 'cqadupstack/physics', 'description': 'CQADupStack: A Benchmark Data Set for Community Question-Answering Research', 'reference': ' 'type': 'Retrieval', 'category... |
def assert_gc_integrity(expect_storage_removed=True):
removed_image_storages = []
remove_callback = model.config.register_image_cleanup_callback(removed_image_storages.extend)
existing_digests = set()
for storage_row in ImageStorage.select():
if storage_row.cas_path:
existing_digests... |
class unit_fusion(nn.Module):
def __init__(self, num_class, in_channels=256):
super(unit_fusion, self).__init__()
self.fc = nn.Linear(in_channels, num_class)
nn.init.normal(self.fc.weight, 0, math.sqrt((2.0 / num_class)))
def forward(self, x1, x2):
y = (x1 + x2)
return se... |
def get_core_candidates(pathtocheck):
cmd = ops.cmd.getDszCommand('dir', path=('"%s"' % os.path.dirname(pathtocheck)), mask=('"%s"' % os.path.basename(pathtocheck)))
obj = cmd.execute()
if cmd.success:
candidates = [f for d in obj.diritem for f in d.fileitem if (f.attributes.directory == 0) if (f.si... |
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